ACM 2018 Hackathon

Team SafeTMap Jonathan Monroe, Dan van Hoesen, Jared Lalmansingh, Esther Koh

Summary: Incorporating crime heat map of St. Louis Metropolitan area in SafeTrek app to visualize the safest travel routes.

SafeTrek has a strong platform which gives users peace of mind and security as they travel. Ideally, people would be able to travel in places where they automatically feel safe. We developed an app to facilitate this process by using machine learning to provide a map for the safest path home. We utilized reported 2017 crime statistics for St. Louis city and selected violent crimes within our target geographic region. We then (intended to) include this in a google maps API to select an alternative path which minimizes the crime risk.

Features St. Louis City police crime reports Jan 2017 - Dec 2017 Parsing & cleaning of crime statistics (csv. files) in Python Crime categories are assigned and given weight factors for their severity Crime location (provided in csv files) converted to longitude and latitude then Google API results using Geocoding. The direction coordinates of the most safe route integrated with the heat map codes to show the crime rate distribution along the route. The heat map is weighed not only on crime severity but also time of the day and the date i.e. the incidences happened yesterday weighed heavier than those from a year ago. Front end developed. Press the button and enter the start location & destination to display the most safe route with mapped crime zones.

Further Considerations User selection & prioritization of crime types Sharing of travel route with family and friends (already a part of SafetyTrek)

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